The scatterplot below contains high school and university grades for 105 computer science majors at Big Ten Universities. We now consider how we could predict a student’s university GPA if we knew his or her high school GPA. b. The best fitting line for this data is as follows: University GPA = 1.097 + 0.675×High School GPA. A student with a high school GPA of 3 would be predicted to have what university GPA?
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
1. The
science majors at Big Ten Universities. We now consider how we could predict a
student’s university GPA if we knew his or her high school GPA.
b. The best fitting line for this data is as follows:
University GPA = 1.097 + 0.675×High School GPA. A student with a high school GPA
of 3 would be predicted to have what university GPA?
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